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This course gives an introduction to standard methods for statistical learning and the mathematical principles underpinning these methods. The purpose is to provide students with a broad introduction to common methods for supervised and unsupervised learning and the mathematical tools used to design and analyse such methods. Although computational experiments are an important component of the course, it is of mathematical nature and emphasises the theory underlying statistical learning.
The following is a rough list of the general topics that will be discussed in the course: Introduction to statistical learning, PAC-learning, half-spaces and perceptrons, regression, artificial neural networks, Bayesian stat & learning, linear methods for supervised classification, tree-based methods, support vector machines, principal component analysis, random forests unsupervised learning, probability in high dimension.
The course webpage is on Canvas:
Publisher's web page for the text book
The authors' web page (data, R scripts, lecture videos)